Using electronic medical record data to shorten diagnostic odysseys for rare genetic disorders in children and adults in two New York City health care settings

使用电子病历数据缩短纽约市两个医疗机构儿童和成人罕见遗传性疾病的诊断过程

• 批准号: 10556355
• 负责人: MANISHA BALWANI
• 金额: $ 33.8万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10556355
• 关键词: Acceleration; Address; Adolescent; Adult; Affect; Age; Algorithms; Ambulatory Care Facilities; Black race; Caring; Child; Childhood; Clinical; Community Hospitals; Computerized Medical Record; DNA; Data; Diagnosis; Diagnostic; Diagnostic tests; Disease; Drops; Education; Electronic Health Record; Evaluation; Family; Genetic; Genetic Services; Goals; Health Personnel; Healthcare; Hispanic; Hospitals; Infant; Internal Medicine; Knowledge; Manuals; Measures; Medical; Medical Genetics; Modeling; Natural Language Processing; New York City; Outcome; Patients; Phase; Pilot Projects; Population; Predictive Value; Process; Rare Diseases; Risk; Site; Surveys; Testing; Time; Toddler; Training; Underserved Population; age group; aged; algorithm development; body system; care burden; cohort; electronic health record system; electronic structure; evaluation/testing; genetic testing; health care settings; improved; multidisciplinary; novel; outpatient programs; outreach; pandemic disease; patient population; pediatric patients; pediatrician; phenotyping algorithm; programs; rare genetic disorder; telehealth; trait; working class; young adult

Rare genetic diseases affect 3.5-6% of the population and are associated with diagnostic odysseys that can last up to decades. As first steps towards shortening diagnostic odysseys for infants and toddlers, we developed rules-based and natural language processing- (NLP-) based algorithms to identify infants and children aged 0–3 years who were typically ill. Our algorithms were accurate for identify atypical ill patients at these ages from electronic health records (EHRs). Cohorts so identified were strongly enriched for patients who had undergone genetic testing. Manual EHR review for such atypically ill patient who had never been evaluated for a rare genetic disease revealed that 52% could appropriately be referred for such an evaluation. During the UG3 phase, we will create a novel outpatient clinic, Mount Sinai Genetics Outreach (GO), staffed with medical geneticists with prior pediatric and internal medicine training, to evaluate patients identified by our EHR phenotyping algorithms. In a pilot study, we will deploy rules- and NLP-based algorithms to identify 200 children aged 0-12 years with >50% risk of having an undiagnosed rare genetic trait. We will survey pediatricians at five practices for baseline knowledge about diagnostic odysseys and genetic testing, provide education about the topic and then study the impact of our algorithm deployment. For patients referred to Mount Sinai GO, we will determine the outcomes of clinical genetic evaluations and diagnostic testing, including impact on subsequent health care. In order to improve our existing algorithms, we developed an automated abstraction engine that identifies patients diagnosed with 164 rare genetic disorders with 83% accuracy. We will expand this to more traits and use their EHR data to improve our pediatric EHR phenotyping algorithms. The goal is to increase sensitivity, currently at ~25%, without dropping precision below 50%. During the UH3 phase, we will deploy our optimized rare disease-detecting algorithms in a non-academic health care setting, Mount Sinai South Nassau Hospital, a non-academic community hospital setting without onsite medical genetic services. Our model will leverage pandemic-accelerated expertise in telehealth to facilitate access of underserved populations to genetics services. Our goal will be to achieve similar sensitivity and precision with our pediatric algorithms as well as a comparably successful referral mechanism. Also, we will extend our clinical rule-based and NLP algorithms to detect adolescent and adult patients likely to have rare genetic disorders and assess the impact of our approach on diagnostic odysseys. We will alter our pediatric rules-based algorithm, first to patients aged 12-21 years and then to younger adults. We will leverage our automated abstraction engine for rare genetic disease for iterative improvements. For adults, we will class traits by organ system in order to improve cohort size/statistical power. Finally, we will assemble and study information about diagnostic odysseys per se, including the impact of our algorithms in shortening them.

罕见的遗传疾病会影响3.5-6％的人口，并且与可以 最后几十年。作为缩短婴儿和幼儿诊断奥德赛的第一步，我们 基于规则的和自然语言处理 - 基于基于规则的语言 - （NLP-）算法，以识别婴儿和 年龄在0-3岁的儿童通常病。我们的算法是准确的，可以在 这些年龄来自电子健康记录（EHR）。如此确定的同类人群强烈富含患者 经过基因检测的人。从未去过的这种非典型病人的手册EHR审查 评估罕见的遗传疾病表明，可以适当地转介52％的评估。 在UG3阶段，我们将创建一个新颖的门诊诊所，西奈山遗传学外展（GO），配备人员 与接受先前儿科和内科培训的医学遗传学家一起评估我们的患者 EHR表型算法。在一项试点研究中，我们将部署基于规则和NLP的算法来识别200 年龄为0-12岁的儿童，其非居住的稀有遗传特征的风险> 50％。我们将调查 儿科医生以五种实践的基线知识，以了解诊断奥德赛和基因检测，提供 有关该主题的教育，然后研究我们算法部署的影响。适用于 西奈山，我们将确定临床遗传评估和诊断测试的结果， 包括对随后的医疗保健的影响。为了改善现有算法，我们开发了 自动抽象引擎鉴定患者患者患有164种稀有遗传疾病，83％ 准确性。我们将将其扩展到更多特征，并使用其EHR数据来改善我们的小儿EHR表型 算法。目的是提高灵敏度，目前约为25％，而不会将精度降至50％以下。 在UH3阶段，我们将在非学术中部署优化的稀有疾病检测算法 卫生保健环境，西奈山拿骚山医院，一个非学术社区医院，没有 现场医疗遗传服务。我们的模型将利用远程医疗中大流行的专业知识 促进服务不足的人群进入遗传学服务。我们的目标是实现类似的灵敏度 以及我们的儿科算法以及相当成功的推荐机制的精确度。另外，我们 将扩展我们的基于临床规则的NLP算法，以检测青少年和可能具有的成年患者 罕见的遗传疾病并评估我们的方法对诊断奥德赛的影响。我们将改变我们的 基于儿科规则的算法，首先是12-21岁的患者，然后是年轻人。我们将利用 我们用于罕见遗传疾病的自动化抽象引擎，以进行迭代改善。对于成年人，我们将上课 器官系统的特征是为了提高队列大小/统计能力。最后，我们将组装和研究 有关诊断奥德赛本身的信息，包括我们算法在缩短它们中的影响。